What are we doing today?

1. R Markdown overview
2. Practice activity or beginner R
3. Continuing practice activity or intermediate/advanced R

What won’t we talk about today?

• Making a website with R Markdown and GitHub (I want to do this in the future!)
• Using R Projects
• Using GitHub for version control and open science (talk to me about this offline)

YAML header

Global options for outputs/formatting

Markup

Plain text formatting allows for conversion to multiple document types

Code chunks

# This is a chunk of R code that adds an image
knitr::include_graphics("images/example_chunk.png")

Output types

• PDF: requires \(\LaTeX\) to compile (TinyTeX distribution)
• HTML
• Microsoft Office (Word/Powerpoint)

YAML specification

• Use output argument in YAML header
• Pay attention to indentation and colons
• Press knit button or command/CTRL-shift-K

Render

Render files in the console with rmarkdown::render(file, output_format) (this is what knit is doing)

Option to create PDF from HTML with pagedown::chrome_print(file)

Built-in templates

• Presentations
  • ioslides and Slidy for HTML
  • Beamer for PDF
• Shiny documents and presentations (interactive)

R packages

• Presentations: revealjs
• CVs: vitae
• Academic posters: posterdown
• APA articles: papaja
• Journal templates: rticles

User-defined templates

\(\LaTeX\) templates (pay attention to $ in templates)

User-defined templates

Word Document: use the Styles Pane and “Update to Match Selection”

YAML parameters

• General YAML parameters
• Template-specific parameters (look at package documentation)

YAML references

These files go in the same place as your .Rmd

• Bibliography: .bib (I use BibDesk for my reference manager)
• Citation style language files: .csl (see my CV and Psychonomics poster repos for customized APA 6 files)
• \(\LaTeX\) styling: .cls
• HTML styling: .css
• Interacting with pandoc: .lua (multiple bibliographies)

YAML references

Example from my CV with both template-specific YAML parameters and YAML references

Inline \(\LaTeX\)  and CSS code

• \(\LaTeX\) with PDFs
  • Calling \(\LaTeX\) packages
  • Using symbols: useful lists here and here
  • Using type-setting commands (e.g., \vspace{12pt})
• CSS with HTML

\(\LaTeX\)

Examples of calling packages in the YAML header and using inline functions from my stats homework

\(\LaTeX\)

Stats homework output

CSS/HTML

Examples from my Psychonomics poster

Editing templates

To make extremely custom edits to templates, sometimes you have to edit the template documents

1. Find out where your computer stores your R packages and edit the template there
2. If the template generates a style document (e.g., .cls) in the directory with your .Rmd file, you can usually edit that without going to the package

Package documents

# Make dataframe with installed packages
pkgs <- installed.packages() %>%
  as.data.frame()

# Pull posterdown package 
pstr <- pkgs %>% 
  select(Package, LibPath, Version, Depends, Imports) %>%
  dplyr::filter(Package == "posterdown")

# Make table
kable(pstr) %>%
  kable_styling(bootstrap_options = "condensed", 
                font_size = 18)

Package documents

• Save the original template and move it to a different location
• Make one change at a time
• Name the updated template with the same name in the same place as the original

Directory documents

Knitting the vitae::awesomecv template created a .cls file that I could edit to change font sizes/colors

Identifying issues

• Warnings vs. errors
• Console vs. chunk
• Markdown environment vs. R environment
• Package specification :: for unloaded packages and conflicting functions

Warnings

Warnings won’t stop your document from compiling, but generally indicate that you should change something in your code

Errors

Chunk error

Markdown/YAML error

R code and environments

Running a chunk executes the code in the console and adds the output to your R environment; however, your R environment is separate from the environment created when “knitting” a document.

R code and environments

# Define new variable y
y <- 100

# When I run this chunk in the console, 
# I get the expected output (150), but
# it fails when I try to knit the document
print(x + y)

Tips

• Clear all variables: rm(ls = list())
• Restart R environment: control/CTRL + fn + shift + F10
• Run all chunks individually in order before compiling to test code
• Search for information
  • Use Help window
  • Search for package in console with ?package
  • Google package and error

Stack Overflow is your friend!

Keys to success

• Treat your data as read-only
• Comment code early and often
• Keep code chunks small
• Label chunks for diagnosing issues
• Nest all files under one directory (if possible)

General information

• Escape characters with a backslash \; this also applies to any chunk output containing strings
• Call R objects and functions in markup/YAML by ~sandwiching~ with backticks ``
• Automatic bracket/quote/asterisk wrapping
• Dollar signs for \(\LaTeX\) math mode (but also be careful with pandoc)
• Line spacing matters in markup

Reference documents

• R Markdown Guide
• R Markdown Cheat Sheet
• R Markdown Reference Guide
• Keyboard shortcuts
• knitr documentation

Other packages/tools

• Sublime Text text editor
• scholar package for automatically downloading citations from Google Scholar
• devtools package for installing packages/plug-ins from GitHub (e.g., papaja)
• wordcountaddin plug-in for checking word count and readability

Updating software/packages

• Update your TeX distribution from the command line
• Update all packages (including rmarkdown) in library with update.packages(path)
• Update individual packages by reinstalling with install.packages(package)
• Update R in the console with updateR package
• Redownload RStudio to update

Pause

Basic R functionality

• Variables
• Functions
• Operations

Variables

The way R stores your information will determine the kinds of functions/operations you can do with it

• Data: dataframes
• Values: lists, vectors, matrices, etc.

# Variables can be numbers, strings, etc.
some_val <- 200
other_val <- TRUE

# Variables can also be lists of numbers, strings, etc.
a_list <- c("a", "b", "c")

# We can also make dataframes (which are essentially special lists)
a_dataframe <- tibble(column_1 = 1:3,
                      column_2 = a_list)

Functions

Functions take a certain number and certain types of “arguments”

• base R functions: part of downloading R
• Packages: need to be installed, then loaded
  • install.packages(package) once (or to update package)
  • library(package) every R session

base R

# base R function
# Count the number of rows in this dataframe
nrow(a_dataframe)

## [1] 3

# If I give nrow() too many arguments, it will throw an error
nrow(a_dataframe, a_list)

# If I give it the wrong kind of argument, it will just return NULL
# Some functions won't run at all with wrong kind of argument
nrow(a_list)

## NULL

Packages

# scholar package 
library(scholar)

# get_publications function
# Pull publications from Google Scholar for Marie Curie
get_publications("EmD_lTEAAAAJ&EmD_lTEAAAAJ&") %>% 
  dplyr::filter(cites > 30) %>% 
  distinct(title, .keep_all = TRUE) %>%
  select(author, title) %>%
  head(2) %>%
  kable()

Operations

• Logical: !, &, &&, |, ||
• Relational: >, <, ==, !=, <=, >=
  • is.na(), exists(), etc. will return TRUE/FALSE values
  • grep(), filter(), str_detect(), etc. use TRUE/FALSE values
• Arithmetic

Reading in data

General parameters for csv files

read.csv("file_name.csv", 
         header = TRUE, 
         stringsAsFactors = FALSE, 
         check.names = FALSE, 
         na.strings = "")

Avoid special characters (including spaces) in file names, directories, and column headers!

Reading in data

• readxl package for Excel spreadsheets
• APIs for direct access to online data
  • qualtRics package for Qualtrics data
  • ggmap package for Google services (geolocation data)
• read_table() from readr package for text files

Tidy R philosophy

• One variable per column
• One observation per row

Manipulating data with tidyr

• %>%: pass the results of one function on to another
• select(): choose columns by name
• mutate(): add/change columns
• filter(): filter for (or out) rows
• group_by() and summarise(): perform operations on groups of data
• gather() and spread() pivot_longer() and pivot_wider(): condense multiple columns into one or the inverse
• separate() and unite(): split a column into multiple or the inverse

Other helpful tidyr functions

• slice(): choose a row
• pull(): choose a column
• Helper functions for select() (e.g., contains())
• join() family of functions: combine datasets based on a shared unique identifier
• union(): combine datasets by rows (column names must be the same)
• replace_na()/drop_na(): alter/remove rows with NA values

Helpful base R functions

• rbind() and cbind(): add rows/columns
• nrow() and ncol(): count rows/columns
• unique(): pull unique values
• Indexing with var$column and var[row, column]
• which() with column/row indexing

Other packages and functions

• tibble package for dataframes with tibble()
• kableExtra() for kable() tables
• ggplot2 package for graphs (cheat sheet here)
• factor() for ordering text labels in graphs
• Use na.rm = TRUE argument (e.g., in mean()) to remove NA values from calculations

Free-response text

• Regular expressions (cheat sheet here)
• Pattern matching
  • Find a pattern: grep(), grepl(), agrep(), agrepl(), str_detect(), etc.
  • Find and change a pattern: sub(), gsub(), replace(), etc.

Dynamic variables

# Variables
vals <- rep(1:3, 3)
name <- "assign_example"

# Assign values to variable name
assign(name, vals)

# Use the variable as usual
assign_example

## [1] 1 2 3 1 2 3 1 2 3

Dynamic variables

# You can also get the new variable from the name
get(name)

## [1] 1 2 3 1 2 3 1 2 3

# You can add to this variable dynamically as well
assign(name, c(get(name), 4:6))

# New output
assign_example

##  [1] 1 2 3 1 2 3 1 2 3 4 5 6

Helpful functions

• apply(), lapply(), sapply(), tapply()
• source() variables from R scripts
• %notin% and %in% (compared to != and ==)

# Example using apply(): go across columns of dataset 
  # and substitute characters
language <- apply(language, 2, 
                  function(x) gsub("\\\\", "", x, fixed = TRUE))

# Source other sripts
source("data_cleaning.R", local = TRUE)

# Helper function
"%notin%" <- Negate("%in%")

# Example from processing pipeline for Qualtrics data
unusable <- c("0","00","107")
dat %>% dplyr::filter(Progress==100 & ID %notin% unusable)

Other tips

• Store ggplot2 parameters in a list()
• list() vs. c()